System for predictive analytics using real-world pharmaceutical transactions

ABSTRACT

A system for predictive analytics using real-world pharmaceutical transactions includes a computer having a processor and memory, a data collection component configured to aggregate data for a plurality of pharmaceutical transactions, and a data reduction module configured to eliminate non-compliant transactions to generate a reduced transaction data set. An analysis module applies multiple linear regression analysis to a portion of the reduced transaction data set to identify key regression variables that correlate with an excess total return to shareholders. The analysis module also applies logistic regression analysis to a portion of the reduced transaction data set to identify key regression variables that correlate with an increased probability of regulatory agency approval. A report generator provides a graphical output of the identified key regression variables and a probability value corresponding to a likelihood of regulatory agency approval.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority from co-pendingprovisional patent application Ser. No. 61/049,914, filed on May 2,2008, entitled System For Predictive Analytics Using Real-WorldPharmaceutical Transactions. Application Ser. No. 61/049,914 is herebyincorporated by reference in its entirety.

BACKGROUND OF THE INVENTION

1. Technical Field

This disclosure relates to predictive analytics in the pharmaceuticalindustry. In particular, this disclosure relates to predicting thelikelihood of success of a real-world pharmaceutical transaction basedon analysis of prior pharmaceutical transactions.

2. Background

Pharmaceutical companies often venture outside of their ownorganizations in search of new assets to develop in their pipeline andto ultimately bring to market. These deals may be in the form of apurchase, license, joint development, strategic arrangement, or otherbusiness transaction. However, the key factors and conditions thatpredict and quantify a successful business transaction or relationshiphave not been established. Typically, pharmaceutical industry expertshave relied on relationships, qualitative evidence, intuition, orexperience-based “rules of thumb” when establishing licensing programsor collaborative arrangements. However, there has been no quantitativeevidence to prove that any of these techniques are successful inselecting deals that create more value. Reliance on such factors doesnot necessarily increase the probability that the transaction will besuccessful. An unmet need exists to identify and quantify the factorsand conditions that correspond to successful pharmaceutical businesstransactions or relationships.

The pressure on pharmaceutical companies to achieve high performance anddeliver new products has never been greater. With revenues erodingbecause of expiring patents and generic competition, companies are in acollective scramble to acquire new compounds. In search of the nextinnovation, companies have been through a decade of whirlwinddeal-making to bring in products from external sources. Whether throughlicensing or more elaborate business development investments, the numberand value of these deals are only expected to increase.

However, only a small portion of these deals result in successfulproducts. To increase the success of these efforts to feed the pipeline,companies have two strategic options. The pharmaceutical companies caneither engage in more deals, or they can become increasingly selectiveand engage in fewer deals. The pharmaceutical industry now reliesheavily on the first strategy—raising its level of investment in thehope of yielding a higher absolute number of successful products.Because there are limited resources for investing in new deals, there isa need for a tool that can assist pharmaceutical companies to be moreselective, and to engage in fewer deals that have a higher probabilityof successful returns.

SUMMARY

The system and method for predictive analytics using real-worldpharmaceutical transactions addresses a second option, namely increasingselectivity in the business development deals a company makes, byidentifying the characteristics that make a deal most likely to succeed.One embodiment of a system for predictive analytics using real-worldpharmaceutical transactions includes a computer having a processor andmemory, a data collection component configured to aggregate data for aplurality of pharmaceutical transactions where the aggregate datacorresponds to publicly-traded financial data based upon a predeterminedtime period surrounding a public announcement of the respectivepharmaceutical transaction.

A data reduction module eliminates non-compliant transactions togenerate a reduced transaction data set and an analysis module appliesmultiple linear regression analysis to a portion of the reducedtransaction data set to identify key regression variables that correlatewith an excess total shareholder return. The key regression variablesthat were statistically significant in this analysis were drugdevelopment phase, deal type, compound or drug type, and therapeuticarea. The analysis module also applies logistic regression analysis to aportion of the reduced transaction data set to identify key regressionvariables that correlate with an increased probability of regulatoryagency approval. A report generator provides a graphical output of theidentified key regression variables and a probability valuecorresponding to a likelihood of regulatory agency approval.

Other embodiments of systems, methods, features, and their correspondingadvantages will be, or will become, apparent to one with skill in theart upon examination of the following figures and detailed description.It is intended that all such additional systems, methods, features, andadvantages be included within this description, be within the scope ofthe invention, and be protected by the following claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The system may be better understood with reference to the followingdrawings and the description, in addition to the presentation sheetsincluded in the appendix, which is incorporated herein in its entirety.The components in the figures are not necessarily to scale, emphasisinstead being placed upon illustrating the principles of the invention.Moreover, in the figures, like-referenced numerals designatecorresponding parts throughout the different views.

FIG. 1 shows a computing platform and environment;

FIG. 2 is a flowchart showing a process identifying key regressionvariables that correlate with an excess total return to shareholders;

FIG. 3 is a flowchart showing a process predicting the likelihood ofeventual FDA approval;

FIG. 4 is a bar chart showing median excess TRS to buyer and seller byclinical phase;

FIG. 5 is a graph illustrating the predictive quality of the stockmarket for phase III compounds; and

FIG. 6 is a graph illustrating the predictive quality of the stockmarket for phase II compounds.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

As shown in FIG. 1, a system 100 for predictive analytics usingreal-world pharmaceutical transactions provides a platform for applyinga statistically rigorous process that identifies “deal” or transactioncharacteristics that are likely to predict success. The specificembodiment of FIG. 1 is a high-level hardware block diagram of acomputer system on which the system 100 for predictive analytics usingreal-world pharmaceutical transactions may be implemented. The system100 for predictive analytics using real-world pharmaceuticaltransactions may be embodied as a system cooperating with computerhardware components and/or as a computer-implemented method.

The system 100 includes a predictive analytic engine or processor 102,which in turn, includes an analysis module 104 or processor, acomparator module 105, a data collection component 106, a data reductionmodule 108, and a report generator 109. The predictive analytic engine102 may be a hardware component and/or may performed processes inhardware, software, or a combination of hardware and software. Thesystem 100 includes a computer or processing system 112, which includesvarious hardware components, such as RAM 114, ROM 116, hard disk storage118, cache memory 120, database storage 122, and the like (also referredto as “memory subsystem” 126). The computer system 112 may include anysuitable processing device 128, such as a computer, microprocessor, RISCprocessor (reduced instruction set computer), CISC processor (complexinstruction set computer), mainframe computer, work station, single-chipcomputer, distributed processor, server, controller, micro-controller,discrete logic computer, and the like, as is known in the art. Forexample, the processing device 128 may be an Intel Pentium®microprocessor, x86 compatible microprocessor, or equivalent device.

The memory subsystem 126 may include any suitable storage components,such as RAM, EPROM (electrically programmable ROM), flash memory,dynamic memory, static memory, FIFO (first-in first-out) memory, LIFO(last-in first-out) memory, circular memory, semiconductor memory,bubble memory, buffer memory, disk memory, optical memory, cache memory,and the like. Any suitable form of memory may be used whether fixedstorage on a magnetic medium, storage in a semiconductor device, orremote storage accessible through a communication link. A user or systemmanager interface 130 may be coupled to the computer system 112 and mayinclude various input devices 136, such as switches selectable by thesystem manager and/or a keyboard. The user interface also may includesuitable output devices 140, such as an LCD display, a CRT, various LEDindicators, and/or a speech output device, as is known in the art.

To facilitate communication between the computer system 112 and externalsources, a communication interface 142 may be operatively coupled to thecomputer system. The communication interface 142 may be, for example, alocal area network, such as an Ethernet network, intranet, Internet, orother suitable network 144. The communication interface 142 may also beconnected to a public switched telephone network (PSTN) 146 or POTS(plain old telephone system), which may facilitate communication via theInternet 144. Dedicated and remote networks may also be employed, andthe system may further communicate with external exchanges and sourcesof information 146. Any suitable commercially-available communicationdevice or network may be used.

FIG. 2 is a flowchart showing a process (Act 200) that identifiesvariables that that correlate with an excess total return toshareholders. The process shown, for example in FIG. 2, may be performedby the predictive analytics engine 102. Data for a plurality ofpharmaceutical transactions is collected (Act 210), and non-complianttransactions are eliminated (Act 220) to generate a reduced transactiondata set. Multiple linear regression analysis is applied to a portion ofthe reduced transaction data set (Act 230) to identify key regressionvariables that correlate with an excess total return to shareholders(Act 240). A report is output (Act 250) that provides a graphical outputof the identified key regression variables and an indication of thecorresponding effect on total return to shareholders.

FIG. 3 is a flowchart showing a process (Act 300) that determines aprobability that a pharmaceutical deal will result in eventual FDAapproval. The process shown, for example in FIG. 3, may be performed bythe predictive analytics engine 102. Data for a plurality ofpharmaceutical transactions is collected (Act 310), and non-complianttransactions are eliminated (Act 320) to generate a reduced transactiondata set. Logistic regression analysis is applied to a portion of thereduced transaction data set (Act 330) to identify key regressionvariables that correlate with an increased probability of obtainingeventual FDA approval (Act 340). A report is output (Act 350) thatprovides a graphical output of the identified key regression variablesand a probability value corresponding to a likelihood of regulatoryagency approval.

Several factors or predictors can indicate which external sourcingarrangements or deal has the best prospects of becoming a winner. In onespecific embodiment, the term “success” may be identified based on twoseparate criteria. A first measure of success is defined as a deal thatincreases shareholder value (excess total return to shareholders (TRS)or ETRS). “Excess” total return to shareholders may be defined as the11-day compounded TRS (as defined by CRSP) minus the 11-day compoundedTRS of a corresponding index. In one embodiment, the normal return isdefined as the TRS AMEX pharma for a pharmaceutical company and AMEXbiotech for a biotech company. An 11-day window is a common time frameused in financial transactions because 11 days is believed to be anadequately long window for capturing the market's reaction to the event,namely the announcement of the deal, while still being sufficientlyshort so as to limit the impact of other corporate events. Data pointsmay be omitted where other major events occurred during the 11-daywindow. Other timeframes may be used, such as a week, a month, aquarter, or a year.

A second measure of success is defined as a deal that results ineventual FDA (Food and Drug Administration) or other regulatory bodyapproval of the drug compound that is the subject of the deal.Preferably, deals where the drug under evaluation are approved orterminated are analyzed. Deals with ongoing research or pending FDAapproval are excluded. Analysis (logistic regression) for Phase I dealsare excluded because few Phase I compounds have received approval fromthe FDA.

In one embodiment, the predictive analytics engine 102 appliesregression analysis (multiple linear regression and logistic regression)to identify factors that correlate with a deal's success. Many factorswere investigated and analyzed. Such factors may include:

-   -   1. Company Acquiring Asset Party Type (CAA)    -   2. Company with Asset Party Type (CWA)    -   3. Parties CAA/CWA    -   4. New Deal Type    -   5. Size    -   6. Weakness of Incentive (Upfront Amount divided by Deal Size)    -   7. Stage of Compound(s)    -   8. Therapeutic Area    -   9. Biologic/Small Molecule/Other    -   10. Transaction # with same partner    -   11. Prior Relationship with Partner    -   12. Total Transaction Frequency CAA    -   13. Total Transaction Frequency CWA

The predictive analytics engine 102 identified four predictors of adeal's success based on regression analysis of the above-enumeratedfactors. The four predictors or “key regression variables” areapplicable to the first measure of success (TRS) and to the secondmeasure of success (eventual FDA approval), and include:

-   -   1. Phase of Development of the Compound    -   2. Deal Type    -   3. Compound Type (Small or Large Molecule)    -   4. Therapeutic Area

The predictive analytics engine 102 includes the analysis module 104 orengine configured to analyze real-world data. The analysis module 104may apply a multiple linear regression process to measure or determinethe occurrence and magnitude of “excess” total return to shareholders(TRS) for both the buyer and the seller (the first measure of success).This process may identify predictors of success, e.g., a higher excesstotal return to shareholders, as certain factors are determined to bestatistically significant through the analysis. For example, in oneembodiment, several factors were determined to be the key predictors ofsuccess. In another embodiment, analysis may be bounded by an 11-daywindow around the deal announcement (five days before and five daysafter). The analysis module 104 may then apply a logistic regressionprocess to determine the type of deal that positively correlates withFDA approval (the second measure of success).

Different regression processes may be used to analyze the data foreither the multiple linear regression process (first measure ofsuccess-excess total return to shareholders) or the logistic regressionprocess (second measure of success-eventual FDA approval). For example,regression trees, Chi-squared regression, least squares regression,regression sum of sequences, error sum of sequences, orthogonalregression, and other regression processes and techniques may be used.

By way of background regarding the first predictor (phase), Phase Icompounds relate to clinical trials that are in the FDA phase I ofclinical testing in human subjects. Normally, a small (20-80) group ofhealthy volunteers is selected. This phase includes trials designed toassess the safety (pharmacovigilance), tolerability, pharmacokinetics,and pharmacodynamics of a drug. These trials are often conducted in anin-patient clinic, where the subject can be observed by full-time staff.The subject who receives the drug is usually observed until severalhalf-lives of the drug have passed. Phase I trials also normally includedose-ranging, referred to as dose escalation, which determines theappropriate dose for therapeutic use. The tested range of doses willusually be a fraction of the dose that causes harm in animal testing.Phase I trials most often include healthy volunteers. However, there aresome circumstances when real patients are used, such as patients whohave end-stage disease and lack other treatment options. This exceptionto the rule most often occurs in oncology (cancer) and HIV drug trials.Volunteers are paid an inconvenience fee for their time spent in thevolunteer centre. Pay ranges from a small amount of money for a shortperiod of residence, to a larger amount depending on the length ofparticipation.

Once the initial safety of the study drug has been confirmed in Phase Itrials, Phase II trials are performed on larger groups (20-300) and aredesigned to assess how well the drug works, as well as to continue PhaseI safety assessments in a larger group of volunteers and patients. Thedevelopment process for a new drug usually fails during Phase II trialswhen the drug is discovered not to work as planned, or if it isdiscovered to have toxic effects. Phase II studies are sometimes dividedinto Phase IIA and Phase IIB. Phase IIA is specifically designed toassess dosing requirements (how much drug should be given), whereasPhase IIB is specifically designed to study efficacy (how well the drugworks at the prescribed dose(s)). Some trials combine Phase I and PhaseII, and test both efficacy and toxicity. Some Phase II trials aredesigned as case series to demonstrate a drug's safety and activity in aselected group of patients. Other Phase II trials are designed asrandomized clinical trials, where some patients receive the drug/deviceand others receive placebo/standard treatment. Randomized Phase IItrials have far fewer patients than randomized Phase III trials.

Phase III studies are randomized controlled multi-center trials on largepatient groups (300-3,000 subjects or more depending upon thedisease/medical condition studied) and are aimed at being the definitiveassessment of how effective the drug is, in comparison with current“gold standard” treatment. Because of their size and comparatively longduration, Phase III trials are the most expensive, time-consuming anddifficult trials to design and run, especially in therapies for chronicmedical conditions. It is common practice that certain Phase III trialswill continue while the regulatory submission is pending at theappropriate regulatory agency. This allows patients to continue toreceive possibly lifesaving drugs until the drug can be obtained bypurchase. Other reasons for performing trials at this stage includeattempts by the sponsor at “label expansion” (to show the drug works foradditional types of patients/diseases beyond the original use for whichthe drug was approved for marketing), to obtain additional safety data,or to support marketing claims for the drug. While not required in allcases, it is typically expected that there be at least two successfulPhase III trials, demonstrating a drug's safety and efficacy, in orderto obtain approval from the appropriate regulatory agencies. The abovedescription of Phase I-Phase III may be found athttp://en.wikipedia.org/wiki/clinical_trial.

With respect to the first predictor involving TRS (first measure ofsuccess), the market clearly rewards buyers of Phase III compounds witha prediction confidence level “p” where p=0.09. Defining what isstatistically significant depends on what the real-life occurrence isexpected to be, and what is normally accepted in the industry. In oneembodiment, a value of p<=0.10 is considered statistically significant,0.10<p<=0.20 is considered moderately significant, and p>0.20 isconsidered marginally significant. The market may react in this waybecause Phase III compounds are closer to generating cash flows. Becausethese products are scarce, they are highly coveted. But this highershareholder return for Phase III compounds does not predict FDAapproval, which is a surprising and unexpected result of the analysis.In fact, many Phase III compounds whose buyers were richly rewarded bythe market failed to receive FDA approval. The lack of correlationbetween the increase in shareholder value for Phase III compounds andFDA approval may indicate that the market is not adequatelydifferentiating these products. The market is so eager for near-termcash flows that it assumes all Phase III products have roughly equalprobability of approval, when in reality they do not.

For Phase II deals, a much stronger link exists between market reactionat deal announcement and the likelihood of FDA approval. This suggeststhat Phase II deals with higher TRS at the deal announcement have ahigher probability of being approved. FIG. 4 shows the excess TRS tobuyers as the left-hand bar of each pair of bars and the excess TRS tosellers as the right-hand bar in each pair of bars.

With respect to the second predictor, the type of deal is shown in Table2 below ranging from a less complex deal to a more complex deal.

TABLE 2 Deal type: simple to complex

Deal complexity is defined as the degree of collaboration betweencompanies.

In one embodiment, the system 100 for predictive analytics usingreal-world pharmaceutical transactions shows unexpected and surprisingresults with respect to the second predictor (deal type) involving TRS(first measure of success). In the hierarchy of deal types, the commonassumption has been that simple asset purchases or licensing deals arelikely to create more value than more collaborative arrangements. Mostlicensing and business development executives prefer arms-lengthtransactions with simpler deal terms. Research and Developmentexecutives similarly prefer the clarity of control that is associatedwith simpler deals.

However, the “simpler is better” assumption has not been confirmed by arigorous study of the historical deal record. The system 100 forpredictive analytics using real-world pharmaceutical transactions, andin particular, the predictive analytics engine 102, indicates thatsimpler transactions are not necessarily advantageous. Although asimpler deal may be less costly, easier to explain to management, andrelatively easy to negotiate and manage, simple deals, such as licensingtransactions, create less shareholder value for the buyer than morecomplex deals, such as collaborative arrangements. Simpler deals alsohave a lower probability of obtaining FDA approval (p=0.06 in Phase II,p=0.20 in Phase III).

More complex deals, such as collaborative arrangements, generally createhigher shareholder value (p=0.01 in Phase I, p=0.34 in Phase II). ForPhase I compounds, collaborative deals rather than simple licensingdeals create more value for the buyer than other types of deals (p=0.02for noncompetitive collaborations, p=0.01 for competitivecollaborations). This may reflect that the seller in thesecollaborations is expressing confidence in the product but simply lackssome of the resources or capabilities needed to carry it to market.

The buying company's shareholders appear to suffer particularly fromlicensing deals for compounds in Phase II (p=0.17). For example, whenShire licensed Pyridorin in Phase II from BioStratum in November 2000 todevelop an extended release version of the diabetic kidney disease drug,Shire shares lost 25.9 percent in value. Once reason for this occurrenceis that in every deal there exists an imbalance of information. Theseller almost always has more information than the buyer. Deals in whichthe seller insists on keeping “skin in the game” may signal thecompany's confidence in the asset. In such deals, the seller prefersrisky profit sharing rather than safe, up-front cash and wants to remaininvolved in the product's development.

In contrast, a seller that prefers to off-load the product in anarms-length transaction, as in a simple licensing deal, is more likelyto have negative information about the asset. What occurs in morecomplex collaborations is that the seller resolves the imbalance ofinformation inherent in every deal. For that reason, these deal typesare favored by the market and predict the likelihood of FDA approval.

With respect to the third predictor for success (compound type), thevalue that comes from a deal involving small or large molecule compoundsdepends on whether a company is the buyer or the seller. Deals for smallmolecule compounds are more likely to generate higher shareholder valuefor buyers (p=0.29, 0.20 for Phase I and Phase III respectively). Dealsfor large molecule compounds generate more value for sellers (p=0.33,0.16 for Phase I and Phase II respectively). Small molecule drug areusually defined as a medicinal drug compound having a molecular weightof less than 1000 Daltons, and typically between 300 and 700 Daltons.These drugs are not made in living cells, but instead are made usinghighly reproducible processes involving chemical analysis.Small-molecule drugs are often defined completely by their atomicstructure rather than by their manufacturing processes, and areadministered orally in pill form. Large molecule drugs are usuallydefined as a drug with a molecular weight larger than 1000 Daltons. Oneexample is biologics. Biologics are made through complex manufacturingprocesses that depend on biological organisms, e.g., yeast or bacteria.It is often difficult to directly compare one biotech product to anotherbecause the complexity of the manufacturing processes is central to theidentity and characteristics of the final biological drug. Some examplesof large molecule drugs include but are not limited to: proteins,antibodies, cytokines, hormones, and stem cells.

One reason underlying this surprising result may be that small moleculecompounds are lower priced, while large molecule compounds demand ahigher premium. To illustrate the different effects on the buyer ofsmall vs. large molecule products, consider these examples. When WatsonPharmaceuticals bought Aslera, a Phase III small molecule compound forthe treatment of lupus, from Genelabs, its shares gained an impressive19.1 percent in value. But when Bristol-Myers Squibb bought Erbitux, aPhase III biologic compound for treatment of colorectal cancer fromImClone, Bristol-Myers Squibb's market value dropped 6.2 percent.

For sellers of small versus large molecule products, the reverse istrue. For example, when Cypress Bioscience sold small moleculeMilnacipran for fibromyalgia syndrome in Phase III to ForestLaboratories in 2004, Cypress Bioscience lost 24.1 percent in totalshareholder return. Compare that to what happened to ImClone when itsigned that deal to sell Erbitux, a biologic compound, to Bristol-MyersSquibb. The sale rewarded ImClone shareholders with a 31.8 percentincrease in value.

Deals involving large molecule compounds are also less likely to receiveFDA approval (p=0.26 in Phase II). Further, compounds out-licensed bybiotechnology companies have a lower probability of gaining FDA approvalthan compounds out-licensed by pharmaceutical companies (p=0.08 forbiotechnology out-licensing to biotechnology and p=0.25 forbiotechnology out-licensing to pharmaceutical companies).

With respect to the fourth predictor for success (therapeutic area), thetypes of therapeutic areas are shown in Table 3 below:

TABLE 3 Therapeutic Group* Therapeutic Area Group 1 Allergy Infection,Anti-inflammatory Pain Dermatology Group 2 Cardiovascular Group 3Respiratory Group 4 Blood Disease/Oncology Group 5 Gynecologic OncologyGroup 6 Kidney Liver Metabolic Transplantation Group 7 Other** *Eachgroup also includes the autoimmune diseases of those therapeutic areas.**This group includes therapeutic areas such as: central nervous system,gastrointestinal, ophthalmology and miscellaneous.

Group 4, Blood Disease/Oncology, is the therapeutic category mostfavored by the market, at least for early phase compounds (p=0.14 inPhase I). But when compounds in this therapeutic category are in thelater stage, the market prefers other therapeutic areas, such as group 1therapeutic category(Allergy/Infection/Anti-inflammatory/Pain/Dermatology) (where p=0.03,0.23 for Phases II and III). For example, consider the Vitaxin deal inFebruary 2001 when MedImmune and Targesome agreed to collaborate on aPhase I compound for targeted anticancer radiotherapy. MedImmune'sshareholders enjoyed a 15.5 percent increase in value because of thepurchase. Also, when Merck bought Phase I AGS-PSCA Antibody from Agensysfor cancer therapy in 2005, Merck gained 7 percent in total shareholderreturn.

However, when Bristol-Myers Squibb bought the late-stage biologiccompound, Erbitux, its shares lost 6.2 percent in value. Likewise, whenAdherex Technologies bought Eniluracil to enhance the effectiveness ofan oncology agent in Phase III from GlaxoSmithKline, Adherex lost 3.1percent in TRS. In that deal, GlaxoSmithKline lost 5.3 percent in TRS.

The market very likely prefers certain therapeutic areas in early phasesbecause riskier compounds with higher market potential, such as those in“blood disease/oncology” are more reasonably priced in these stages. Butcancer drugs that have reached Phase III carry a heavy premium becausethey are highly sought after. The market does not reward buyers of thesecompounds, believing that the asset may be overpriced.

In deals for allergy/infection/anti-inflammatory/pain/dermatologycompounds, the market is more likely to favor late stage deals. Forcompounds in these therapeutic areas, there is less unmet demand, so theproduct is more reasonably priced in later stages. This isunderstandable because those compounds may not be viewed as especiallyinnovative or their market may be saturated.

In another embodiment, the system 100 for predictive analytics usingreal-world pharmaceutical transactions and/or the predictive analyticsengine 102 have also identified the four predictors of a deal's successwith respect to FDA approval (second measure of success). These fourpredictors or drivers of commercial success correlate positively with ahigher probability of FDA approval.

With respect to the second measure of success (FDA approval) and thefirst predictor (phase), processing based on the above example using thetransaction base of 18,194 (and subsequently reduced to eliminatenon-compliant transactions, described below) determined that 28compounds were approved vs. 17 terminated, or 62% of compounds in PhaseIII deals were approved (compounds in development or pending approvalwere excluded). For Phase II deals, 14 compounds were approved vs. 39terminated, or 26% of compounds in Phase II deals were approved. Comparethese percentages with industry success rates of Phase III, which havefallen from 75% to about 60% over the last 5 years, and compare to theindustry success rates of Phase II, which have fallen from 40% to 25%over the last 5 years.

With respect to the second measure of success (FDA approval) and thesecond predictor (deal type), all deal types except for simple licensingdeals correlate positively with an increased probability of FDAapproval. Simple licensing deal have a negative correlation.

With respect to the second measure of success (FDA approval) and thethird predictor (compound type), for Phase II deals, small moleculedeals had a higher probability of FDA approval than biologics.

With respect to the second measure of success (FDA approval) and thefourth predictor (therapeutic area), for Phase III deals, alltherapeutic areas except for Group 1 (Allergy/Pain/Infection), Group 4(Blood Disease/Oncology), and Group 6 (Kidney/Liver/Metabolic) correlatepositively with an increased probability of FDA approval. For Phase IIdeals, Group 1 (Allergy/Pain/Infection) deals have a higher probabilityof FDA approval than all other therapeutic areas.

Using logistic regression based on the predictors identified above, aprobability of FDA approval can be determined. Table 4 below representsa spreadsheet for calculating a probability value for Phase II deals.

TABLE 4 PHASE II PROBABILITY PREDICTOR

The spreadsheet formula for Cell C11 is shown below:EXP(H4+SUMPRODUCT(C4:C9,H5:H10))/(1+EXP(H4+SUMPRODUCT(C4:C9,H5:H10)))

The above spreadsheet formula in cell C11 indicates that for the valueof the variables shown, there is a 64% probability that FDA approvalwill be obtained. Logistic regression was performed on Phase II andPhase III data, and on the combined dataset. Depending on how thelogistic regression is run, different regression equations emerge withdistinct coefficients for the statistically significant variables. Suchvariables may be entered into column G with their coefficients enteredinto column H each time a logistic regression model for the dataset iscreated. The significance of this is 1) as more deal data is gathered,the predictor model becomes more accurate, and 2) depending on thedataset that is used for analysis, the key drivers of success for a dealmay change. For example, if 10,000 more deals are added to the dataset,it is possible that other factors, such as, for example, priorrelationship with partner, may become statistically significant and maybecome a key success driver in deals. The formula is in the form ofe^(x)/(1+e^(x)), where “x” is the intercept value plus the sum ofproducts. The descriptions of the variables in cells B4-B9 are shown inTable 5 below. The corresponding parameter values are located in cellsH5-H9, and the intercept value is located in cell H4:

TABLE 5 VARIABLE NAME DESCRIPTION DUM_CAA_PARTY_PHARMA The input valueof 1 in cell C4 indicates a pharmaceutical deal. DUM_DT_LICENSE Theinput value of 0 in cell C5 indicates that this is not a licensing deal.DUM_TA_GROUP_1 The input value of 0 in cell C6 indicates that the dealdoes not involve therapeutic group 1, as shown above in Table 3DUM_TA_GROUP_4 The input value of 1 in cell C7 indicates that the dealdoes involve therapeutic group 1, as shown above in Table 3.DUM_BVSM_BIO The input value of 1 in cell C8 indicates that the compoundis biologic. TOTAL_TXN_FREQ_CAA The input value of 1 in cell C9indicates a pharmaceutical deal.

Table 6 below represents a spreadsheet for calculating a probabilityvalue for Phase III deals. The spreadsheet formula is the same as shownabove:

TABLE 6 PHASE III PROBABILITY PREDICTOR

The above spreadsheet formula in cell C11 indicates that for the valueof the variables shown, there is a 49% probability that FDA approvalwill be obtained. The formula is in the form of e^(x)/(1+e^(x)), where“x” is the intercept value plus the sum of products. The descriptions ofthe variables in cells B4-B9 are shown in Table 7 below. Thecorresponding parameter values are located in cells H5-H9, and theintercept value is located in cell H4:

TABLE 7 VARIABLE NAME DESCRIPTION DUM_CAA_PARTY_BIOTEC The input valueof 1 in cell C4 indicates a biotechnology deal. DUM_DT_LICENSE The inputvalue of 1 in cell C5 indicates that this is not a licensing deal.WEAKNESS_OF_INCENTIVE The input value of 0.26 in cell C6 indicates anaverage value of the incentive DUM_TA_GROUP_1 The input value of 1 incell C7 indicates that the deal does involve therapeutic group 1, asshown above in Table 3. DUM_TA_GROUP_4 The input value of 0 in cell C8indicates that the deal does not involve therapeutic group 4, as shownabove in Table 3. DUM_TA_GROUP_6 The input value of 0 in cell C9indicates that the deal does not involve therapeutic group 6, as shownabove in Table 3.

In one example, the system 100 for predictive analytics using real-worldpharmaceutical transactions may process a large number of pharmaceuticaldeals or transactions. The number of deals analyzed may be reduced toexclude non-compliant deals. In one specific example, the datacollection component 106 inspected data from 18,194 deals during aten-year period from about 1997 to about 2006. Data was extracted fromRecombinant Capital's rDNA's database. The data reduction module 108eliminated non-compliant transactions to generate a reduced transactiondata set. Data regarding total return to shareholders was obtained fromthe Center for Research in Security Prices (CRSP) and Yahoo! Finance.

With regard to the first measure of success (TRS) and application ofmultiple linear regression, certain non-compliant deals were excluded toobtain the reduced transaction data set, as shown in Table 8 below. Inthis specific example, the initial data set of 18,194 deals was reducedto about 355 deals before applying statistical analyses. For the dealsthat had TRS information, multiple linear regression was applied tomeasure the magnitude of excess TRS. For the deals in Phase II and PhaseIII that had approval information (approved or rejected), logisticregression was applied to measure the likelihood of eventual FDAapproval.

TABLE 8 International International deals directed to development orcommercialization outside of the U.S. were excluded. Of these deals,those that also had development or commercialization inside the U.S.were not excluded Multiple Phase Deals where compounds were in multiplephases were excluded to permit more cogent insight about inter-phasedifferences. Drugs Approved Drugs that already received FDA approvalwere excluded Parties Deals not between pharmaceutical companies andbiotechnology companies were excluded Stock Information Deals wherecompanies that were not listed on a stock exchange or did not have stockprice information available were excluded. Manufacturing Deals formanufacturing or contract research organization services were excluded

With regard to the second measure of success (FDA approval) andapplication of a logistic regression process to determine the type ofdeal that positively correlates with FDA approval, deals with “ongoingresearch” or “pending FDA approval” were excluded. An FDA response wasrequired to serve as the dependant variable. The reduced transactiondata set corresponding to the second measure of success was augmentedwith drug approval data from Drugs@FDA. Note that the reducedtransaction data set used in determining the first measure of success(TRS) may have a different size than the reduced transaction data setused in determining the second measure of success (FDA approval) due tothe different exclusion criteria.

The report generator 109 (FIG. 1) provides a graphical or hard copyoutput indicating the predictors or success, excess total return toshareholders, and/or the probability value associated with thelikelihood of FDA approval.

Note that although the system 100 for predictive analytics usingreal-world pharmaceutical transactions and/or the predictive analyticsengine 102 predicts success of a deal based on FDA approval as onemeasure, any regulatory body approval may be used as the measure.Accordingly, other regulatory agencies may include the Therapeutic GoodsAdministration (TGA-Australia), the European Medicines Agency(EMEA-European Union), the Japan Ministry of Health, Labour, and Welfare(MHLW), and the like.

The predictors of success determined by the system 100 for predictiveanalytics using real-world pharmaceutical transactions are in someinstances, surprising, unexpected, and counter-intuitive. For example,the analysis results indicate that competitive collaborations, notsimple licensing deals, are most likely to increase shareholder valueand gain FDA approval. Other analysis results indicate that the stockmarket clearly rewards buyers of Phase III compounds. However, theanalysis results also indicate that there is no correlation between anenthusiastic market reaction and eventual FDA approval. This issurprising and counter-intuitive.

Further, when considering what is good for a buyer versus what is goodfor a seller in the making of a pharmaceutical deal, the answer dependson whether the deal is for a small or a large molecule compound.Accordingly, the system 100 for predictive analytics using real-worldpharmaceutical transactions can assess a deal's potential for success,and it can provide insight on how a strategic approach to licensing andbusiness development can increase a company's competitive power andaccelerate its drive for high performance.

The predictors of success determined by the system 100 for predictiveanalytics using real-world pharmaceutical transactions can maximizeshareholder value through licensing and business development investmentsand improve the effectiveness of a company's licensing and businessdevelopment strategy by sharpening the ability to select from theprospects at hand. A company may do this in a short-term timeframe andin a long-term timeframe.

In the short term, the most effective way for companies to driveshareholder value and improve the likelihood of FDA approval is to formcollaborative partnerships (second predictor). The success of thesecollaborations is attributed to the confidence in the product expressedby the seller's willingness to put “skin in the game.”

In the longer term, evaluation of the results is more complex. If thelevel of investment required to acquire attractive product innovationscontinues to grow at the current rate, many companies will findthemselves in an “arms race” of continual escalation of licensing andbusiness development investments, and participation in this race soonbecomes unfeasible. Thus, companies must be skillful in choosing theright deals, and must make difficult and judicious choices about whichdeals to pursue.

However, in the deal selection process what is good for the buyer may bedifferent from what is good for the seller. Results from the system 100for predictive analytics using real-world pharmaceutical transactions inone embodiment indicate that buyers should take the following actions:

-   -   1. Seek sellers who want to partner in the development or        commercialization of the compound. This signals a high degree of        confidence in the asset, and is especially important in        late-stage products.    -   2. Consider buying Phase III compounds if they can be found, but        approach these expensive deals cautiously. While these purchases        increase shareholder value in the short term, there is no        correlation between the market's enthusiasm and FDA approval for        Phase III products.    -   3. Buy blood disease/oncology products in Phase I. But be        judicious in later phases of development when these products        command a much higher premium.    -   4. Buy allergy/infection/anti-inflammatory/pain/dermatology        compounds in later phases of development. At this point they are        reasonably priced and have lower risk.    -   5. Be cautious about large molecule compounds because of their        high prices.

Results from the system 100 for predictive analytics using real-worldpharmaceutical transactions in one embodiment indicate that sellersshould take the following actions:

-   -   1. Raise the price of Phase III compounds even more. The market        is willing to reward buyers at this point and sellers should        factor that into the price.    -   2. Sell large molecule compounds in Phases I and II of        development.

The results shown in the following tables (Table 9-Table 19) may begenerated by the comparator module 105 of the predictive analyticsengine 102. Table 9 provides a summary illustrating how buyers canmaximize TRS with regard to phase I deals. The table indicates whetherTRS increases or decreases based on the illustrated factors, andprovides a corresponding confidence level.

TABLE 9 Does the Total Return to Shareholders to the BUYER increase ordecrease? Factors Phase 1 P-Value Deal Type: License ⇑ 0.01 Deal Type:Non-Competitive ⇑ 0.02 Collaboration Deal Type: CompetitiveCollaboration ⇑ 0.01 TA: Blood/Oncology ⇑ 0.14 TA:Allergy/Pain/Infection N/A N/A Compound Type: Small Molecule ⇑ 0.29Note: N/A denotes factor not statistically significant for that phase.

Table 10 provides a summary illustrating how buyers can maximize TRSwith regard to phase II deals. The table indicates whether TRS increasesor decreases based on the illustrated factors, and provides acorresponding confidence level.

TABLE 10 Does the Total Return to Shareholders to the BUYER increase ordecrease? Factors Phase 2 P-Value Deal Type: License ↓ 0.17 Deal Type:Non-Competitive N/A N/A Collaboration Deal Type: CompetitiveCollaboration N/A N/A TA: Blood/Oncology N/A N/A TA:Allergy/Pain/Infection ⇑ 0.03 Compound Type: Small Molecule N/A N/ANote: N/A denotes factor not statistically significant for that phase.

Table 11 provides a summary illustrating how buyers can maximize TRSwith regard to phase III deals. The table indicates whether TRSincreases or decreases based on the illustrated factors, and provides acorresponding confidence level.

TABLE 11 Does the Total Return to Shareholders to the BUYER increase ordecrease? Factors Phase 3 P-Value Phase: Phase 3 ⇑  0.09* Deal Type:License N/A N/A Deal Type: Non-Competitive N/A N/A Collaboration DealType: Competitive Collaboration ⇑ 0.34 TA: Blood/Oncology N/A N/A TA:Allergy/Pain/Infection ⇑ 0.23 Compound Type: Small Molecule ⇑ 0.20 Note:N/A denotes factor not statistically significant for that phase. *Verylimited evidence, model with all combined phases had a poor fit.

Table 12 provides a summary illustrating how buyers can maximize TRSwith regard to phase I, II, and III deals. The table indicates whetherTRS increases or decreases in the specific phase based on theillustrated factors, and provides a corresponding confidence level.

TABLE 12 Does the Total Return to Shareholders to the BUYER increase ordecrease? Factors Phase 1 Phase 2 Phase 3 P-Value Phase: Phase 1 vs. 2vs. 3 N/A N/A ⇑  0.09* Deal Type: License ⇑ ↓ N/A 0.01, 0.17 Deal Type:Non-Competitive ⇑ N/A N/A 0.02 Collaboration Deal Type: Competitive ⇑N/A ⇑ 0.01, 0.34 Collaboration TA: Blood/Oncology ⇑ N/A N/A 0.14 TA:Allergy/Pain/Infection N/A ⇑ ⇑ 0.03, 0.23 Compound Type: Small ⇑ N/A ⇑0.29, 0.20 Molecule *Very limited evidence, model with all combinedphases had a poor fit. Note: N/A denotes factor not statisticallysignificant for that phase.

Table 13 provides a summary illustrating how sellers can maximize TRSwith regard to phase I deals. The table indicates whether TRS increasesor decreases based on the illustrated factors, and provides acorresponding confidence level.

TABLE 13 Does the Total Return to Shareholders to the SELLER increase ordecrease? Factors Phase 1 P-Value Phase: Phase 1 ↓ 0.03 Deal Type:Competitive Collaboration ↓ 0.29 Deal Type: Non-Competitive N/A N/ACollaboration TA: Allergy/Pain/Infection ↓ 0.02 TA: Blood/Oncology ↓0.02 TA: Kidney/Liver/Metabolic ↓ 0.08 Compound Type: Biologic ⇑ 0.33Deal Size: Larger ⇑ 0.03 Note: N/A denotes factor not statisticallysignificant for that phase. Phase I and Phase II both have a negativeinfluence on TRS, but Phase I creates even less value than Phase II.

Table 14 provides a summary illustrating how sellers can maximize TRSwith regard to phase II deals. The table indicates whether TRS increasesor decreases based on the illustrated factors, and provides acorresponding confidence level.

TABLE 14 Does the Total Return to Shareholders to the SELLER increase ordecrease? Factors Phase 2 P-Value Phase: Phase 2 ↓ 0.03 Deal Type:Competitive Collaboration N/A N/A Deal Type: Non-Competitive ↓ 0.02Collaboration TA: Allergy/Pain/Infection N/A N/A TA: Blood/Oncology N/AN/A TA: Kidney/Liver/Metabolic N/A N/A Compound Type: Biologic ⇑ 0.16Deal Size: Larger N/A N/A Note: N/A denotes factor not statisticallysignificant for that phase. Phase I and Phase II both have a negativeinfluence on TRS, but Phase I creates even less value than Phase II.

Table 15 provides a summary illustrating how sellers can maximize TRSwith regard to phase III deals. The table indicates whether TRSincreases or decreases based on the illustrated factors, and provides acorresponding confidence level.

TABLE 15 Does the Total Return to Shareholders to the SELLER increase ordecrease? Factors Phase 3* P-Value Phase: Phase 3 N/A N/A Deal Type:Competitive Collaboration N/A N/A Deal Type: Non-Competitive N/A N/ACollaboration TA: Allergy/Pain/Infection ↓ 0.15 TA: Blood/Oncology ↓0.11 TA: Kidney/Liver/Metabolic N/A N/A Compound Type: Biologic N/A N/ADeal Size: Larger N/A N/A Note: N/A denotes factor not statisticallysignificant for that phase. *Very limited evidence, as Phase III sellermodel had a poor fit.

Table 16 provides a summary illustrating how sellers can maximize TRSwith regard to phase I, II, and III deals. The table indicates whetherTRS increases or decreases in the specific phase based on theillustrated factors, and provides a corresponding confidence level.

TABLE 16 Does the Total Return to Shareholders to the SELLER increase ordecrease? Factors Phase 1 Phase 2 Phase 3* P-Value Phase: Phase 1 vs. 2vs. 3 ↓ ↓ N/A 0.03, 0.03 Deal Type: Competitive ↓ N/A N/A 0.29Collaboration Deal Type: Non-Competitive N/A ↓ N/A 0.02 CollaborationTA: Allergy/Pain/Infection ↓ N/A ↓ 0.02, 0.15 TA: Blood/Oncology ↓ N/A ↓0.02, 0.11 TA: Kidney/Liver/Metabolic ↓ N/A N/A 0.08 Compound Type:Biologic ⇑ ⇑ N/A 0.33, 0.16 Deal Size: Larger ⇑ N/A N/A 0.03 *Verylimited evidence, as Phase III seller model had a poor fit. Note: N/Adenotes factor not statistically significant for that phase.

Table 17 provides a summary illustrating the factors that influence theprobability of drug approval with regard to phase II deals. The tableindicates whether the probability increases or decreases based on theillustrated factors, and provides a corresponding confidence level.

TABLE 17 Does the Probability of Drug Approval increase or decrease witheach factor? Factors Phase 2 P-Value Phase: Phase 2 vs. Phase 3 ↓ 0.0005Deal Type: License ↓ 0.06 TA: Allergy/Pain/Infection ⇑ 0.20 TA:Blood/Oncology ⇑ 0.30 TA: Kidney/Liver/Metabolic N/A N/A Compound Type:Biologic ↓ 0.26 Note: Logistic regression not performed for Phase Ideals; N/A denotes factor not statistically significant for that phase.

Table 18 provides a summary illustrating the factors that influence theprobability of drug approval with regard to phase III deals. The tableindicates whether the probability increases or decreases based on theillustrated factors, and provides a corresponding confidence level.

TABLE 18 Does the Probability of Drug Approval increase or decrease witheach factor? Factors Phase 3 P-Value Phase: Phase 3 N/A N/A Deal Type:License ↓ 0.20 TA: Allergy/Pain/Infection ↓ 0.19 TA: Blood/Oncology ↓0.03 TA: Kidney/Liver/Metabolic ↓ 0.17 Compound Type: Biologic N/A N/ANote: Logistic regression not performed for Phase I deals;

N/A denotes factor not statistically significant for that phase.

Table 19 provides a summary illustrating the factors that influence theprobability of drug approval with regard to in-licensed deals for phaseII and phase III. The table indicates whether the probability increasesor decreases based on the illustrated factors, and provides acorresponding confidence level.

TABLE 19 Does the Probability of Drug Approval increase or decrease witheach factor? Factors Phase 2 Phase 3 P-Value Phase: Phase 2 vs. Phase 3↓ N/A 0.0005 Deal Type: License ↓ ↓ 0.06, 0.20 TA:Allergy/Pain/Infection ⇑ ↓ 0.20, 0.19 TA: Blood/Oncology ⇑ ↓ 0.30, 0.03TA: Kidney/Liver/ N/A ↓ 0.17 Metabolic Compound Type: Biologic ↓ N/A0.26 Note: Logistic regression not performed for Phase I deals; N/Adenotes factor not statistically significant for that phase.

FIG. 5 graphically illustrates that higher shareholder return is notnecessarily a good predictor of eventual FDA approval for phase IIIcompounds. For example, the deals having the top 25% in TRS had fewerFDA approvals than deals in the bottom 25% in TRS.

However, FIG. 6 graphically illustrates that for phase II deals, a muchstronger link between market reaction at the time of deal announcementand the likelihood of FDA approval exists. In this case, for example,the deals having the top 25% in TRS had a greater number of FDAapprovals than deals in the bottom 25% in TRS.

The logic, circuitry, and processing described above may be encoded in acomputer-readable medium such as a CDROM, disk, flash memory, RAM orROM, an electromagnetic signal, or other machine-readable medium asinstructions for execution by a processor. Alternatively oradditionally, the logic may be implemented as analog or digital logicusing hardware, such as one or more integrated circuits, or one or moreprocessors executing instructions; or in software in an applicationprogramming interface (API) or in a Dynamic Link Library (DLL),functions available in a shared memory or defined as local or remoteprocedure calls; or as a combination of hardware and software.

The logic may be represented in (e.g., stored on or in) acomputer-readable medium, machine-readable medium, propagated-signalmedium, and/or signal-bearing medium. The media may comprise any devicethat contains, stores, communicates, propagates, or transportsexecutable instructions for use by or in connection with an instructionexecutable system, apparatus, or device. The machine-readable medium mayselectively be, but is not limited to, an electronic, magnetic, optical,electromagnetic, or infrared signal or a semiconductor system,apparatus, device, or propagation medium. A non-exhaustive list ofexamples of a machine-readable medium includes: a magnetic or opticaldisk, a volatile memory such as a Random Access Memory “RAM,” aRead-Only Memory “ROM,” an Erasable Programmable Read-Only Memory (i.e.,EPROM) or Flash memory, or an optical fiber. A machine-readable mediummay also include a tangible medium upon which executable instructionsare printed, as the logic may be electronically stored as an image or inanother format (e.g., through an optical scan) and then compiled and/orinterpreted or otherwise processed. The processed medium may then bestored in a computer and/or machine memory.

The systems may include additional or different logic and may beimplemented in many different ways. A controller may be implemented as amicroprocessor, microcontroller, application specific integrated circuit(ASIC), discrete logic, or a combination of other types of circuits orlogic. Similarly, memories may be DRAM, SRAM, Flash, or other types ofmemory. Parameters (e.g., conditions and thresholds) and other datastructures may be separately stored and managed, may be incorporatedinto a single memory or database, or may be logically and physicallyorganized in many different ways. Programs and instruction sets may beparts of a single program, separate programs, or distributed acrossseveral memories and processors.

While various embodiments of the invention have been described, it willbe apparent to those of ordinary skill in the art that many moreembodiments and implementations are possible within the scope of theinvention. Accordingly, the invention is not to be restricted except inlight of the attached claims and their equivalents.

We claim:
 1. A system for predictive analytics for a target entity usingreal-world pharmaceutical transactions, comprising: a computer having aprocessor and memory; a data collection component controlled by theprocessor, configured to aggregate data for a plurality ofpharmaceutical transactions external to the target entity, the aggregatedata corresponding to publicly-traded financial data based upon apredetermined time period surrounding a public announcement of therespective pharmaceutical transaction; a data reduction modulecontrolled by the processor, configured to eliminate non-complianttransactions to generate a reduced transaction data set; an analysismodule controlled by the processor, configured to apply multiple linearregression analysis to a first portion of the reduced transaction dataset to identify key regression variables that correlate with an excesstotal return to shareholders; the analysis module configured to applylogistic regression analysis to a second portion of the reducedtransaction data set to identify key regression variables that correlatewith an increased probability of regulatory agency approval; the keyregression variables including drug clinical trial phase including phaseI trials, phase II trials, and phase III trials; deal type; compoundtype, which indicates whether the respective pharmaceutical transactioninvolves a small molecule compound having a molecular weight of lessthan or equal to 1000 Daltons or a large molecule compound having amolecular weight greater than 1000 Daltons; and therapeutic area; acomparator module that based on the regression analysis applied,determines that: an increased total return to shareholders for a buyercorrelates with a respective pharmaceutical transaction involving thephase III trial; an increased total return to shareholders for a buyercorrelates with a respective pharmaceutical transaction involving aphase I trial wherein the deal type is a license, a non-competitivecollaboration, or a competitive collaboration; a decreased total returnto shareholders for a buyer correlates with a respective pharmaceuticaltransaction involving a phase II trial wherein the deal type is thelicense; and a report generator adapted to provide a graphical output ofthe identified key regression variables and an indication of thecorresponding effect on total return to shareholders, and a probabilityvalue corresponding to a likelihood of regulatory agency approval. 2.The system according to claim 1, wherein the predetermined time periodis an eleven day time period.
 3. The system according to claim 1,wherein the predetermined time period ranges from one week to one month.4. The system according to claim 1, wherein the predetermined timeperiod is less than one year.
 5. The system according to claim 1,wherein the non-compliant transactions are selected from the groupconsisting of international deals, dealing with multiple phasecompounds, drugs having regulatory body approval, deals not betweenpharmaceutical companies and biotechnology companies, deals involvingcompanies not listed on a stock exchange, and deals directed tomanufacturing.
 6. The system according to claim 1, wherein the drugclinical trial phase indicates whether the respective pharmaceuticaltransaction involves drugs currently in phase I clinical trials, phaseII clinical trials, or phase III clinical trials.
 7. The systemaccording to claim 1, wherein the deal type corresponds to a level ofcollaboration between parties to a respective transaction.
 8. The systemaccording to claim 7, wherein the comparator module determines that: anincreased total return to shareholders for a buyer correlates with arespective pharmaceutical transaction involving a phase I compound wherea level of collaboration includes a license, a non-competitivecollaboration, and a competitive collaboration; an increased totalreturn to shareholders for a buyer correlates with a respectivepharmaceutical transaction involving a phase III compound where a levelof collaboration is the competitive collaboration; a decreased totalreturn to shareholders for a buyer correlates with a respectivepharmaceutical transaction involving a phase II compound where a levelof collaboration is the license; a total return to shareholders for abuyer does not correlate with a respective pharmaceutical transactioninvolving a phase II compound where a level of collaboration is thenon-competitive collaboration or the competitive collaboration; and atotal return to shareholders for a buyer does not correlate with arespective pharmaceutical transaction involving a phase III compoundwhere a level of collaboration is the non-competitive collaboration orthe license.
 9. The system according to claim 7, wherein the comparatormodule determines that: a decreased total return to shareholders for aseller correlates with a respective pharmaceutical transaction involvinga phase I compound where a level of collaboration is a competitivecollaboration; a decreased total return to shareholders for a sellercorrelates with a respective pharmaceutical transaction involving aphase II compound where a level of collaboration is a non-competitivecollaboration; a total return to shareholders for a seller does notcorrelate with a respective pharmaceutical transaction involving a phaseII compound or a phase III compound where a level of collaboration isthe competitive collaboration; and a total return to shareholders for aseller does not correlate with a respective pharmaceutical transactioninvolving a phase I compound or a phase III compound where a level ofcollaboration is the non-competitive collaboration.
 10. The systemaccording to claim 1, wherein the therapeutic area indicates whether therespective pharmaceutical transaction involves a group 1 therapeuticarea (allergy/infection/anti-inflammatory/pain/dermatology), a group 2therapeutic area (cardiovascular), a group 3 therapeutic area(respiratory), a group 4 therapeutic area (blood disease/oncology), agroup 5 therapeutic area (gynecologic oncology), a group 6 therapeuticarea (kidney/liver/metabolic), or a group 7 therapeutic area (autoimmunediseases of groups 1-6).
 11. The system according to claim 10, whereinthe comparator module determines that: an increased total return toshareholders for a buyer correlates with a respective pharmaceuticaltransaction involving a phase I compound where the therapeutic area is agroup 4 therapeutic area; an increased total return to shareholders fora buyer correlates with a respective pharmaceutical transactioninvolving a phase II compound or a phase III compound, where thetherapeutic area is a group 1 therapeutic area; a total return toshareholders for a buyer does not correlate with a respectivepharmaceutical transaction involving a phase II compound or a phase IIIcompound, where the therapeutic area is a group 4 therapeutic area; anda total return to shareholders for a buyer does not correlate with arespective pharmaceutical transaction involving a phase I compound wherethe therapeutic area is a group 1 therapeutic area.
 12. The systemaccording to claim 1, wherein the comparator module determines that: anincreased total return to shareholders for a buyer correlates with arespective pharmaceutical transaction involving a phase III compound;and a total return to shareholders for a buyer has no correlation for arespective pharmaceutical transaction involving a phase II compound or aphase III compound.
 13. The system according to claim 1, wherein thecomparator module determines that: an increased total return toshareholders for a buyer correlates with a respective pharmaceuticaltransaction involving a phase I compound or a phase III compound wherethe compound type is a small molecule compound; and a total return toshareholders for a buyer does not correlate with a respectivepharmaceutical transaction involving a phase II compound where thecompound type is a small molecule compound.
 14. The system according toclaim 1, wherein the comparator module determines that: a decreasedtotal return to shareholders for a seller correlates with a respectivepharmaceutical transaction involving a phase II compound or a phase IIIcompound; and a total return to shareholders for a seller does notcorrelate with a respective pharmaceutical transaction involving a phaseIII compound.
 15. A method for predictive analytics for a target entityusing real-world pharmaceutical transactions, comprising: providing acomputer having a processor and memory; collecting, by the processor,data for a plurality of pharmaceutical transactions external to thetarget entity, the aggregate data corresponding to publicly-tradedfinancial data based upon a predetermined time period surrounding apublic announcement of the respective pharmaceutical transaction;eliminating non-compliant transactions from the plurality ofpharmaceutical transactions to generate a reduced transaction data set;applying multiple linear regression, by the processor, to a firstportion of the reduced transaction data set to identify key regressionvariables that correlate with an excess total return to shareholders;applying logistic regression analysis, by the processor, to a secondportion of the reduced transaction data set to identify key regressionvariables that correlate with an increased probability of regulatoryagency approval; the key regression variables including drug clinicaltrial phase including phase I trials, phase II trials, and phase IIItrials; deal type; compound type, which indicates whether the respectivepharmaceutical transaction involves a small molecule compound having amolecular weight of less than or equal to 1000 Daltons or a largemolecule compound having a molecular weight greater than 1000 Daltons;therapeutic area; determining, by the processor, using the regressionanalysis that: an increased total return to shareholders for a buyercorrelates with a respective pharmaceutical transaction involving thephase III trial; an increased total return to shareholders for a buyercorrelates with a respective pharmaceutical transaction involving aphase I trial wherein the deal type is a license, a non-competitivecollaboration, or a competitive collaboration; a decreased total returnto shareholders for a buyer correlates with a respective pharmaceuticaltransaction involving a phase II trial wherein the deal type is thelicense; and generating a graphical output of the identified keyregression variables and an indication of the corresponding effect ontotal return to shareholders, and a probability value corresponding to alikelihood of regulatory agency approval.
 16. The method according toclaim 15, wherein the regulatory agency is the U.S. Food and DrugAdministration (FDA), Therapeutic Goods Administration (TGA-Australia),the European Medicines Agency (EMEA-European Union), or the JapanMinistry of Health, Labour, and Welfare (MHLW).
 17. The method accordingto claim 15, further determining that: a probability of receiving theregulatory body approval for phase II compounds negatively correlateswith a respective pharmaceutical transaction when a level ofcollaboration between parties of the transaction is a license; aprobability of receiving the regulatory body approval for phase IIcompounds positively correlates with a respective pharmaceuticaltransaction when a therapeutic area of the transaction is group 1therapeutic area (allergy/infection/anti-inflammatory/pain/dermatology)or a group 4 therapeutic area (blood disease/oncology) a probability ofreceiving the regulatory body approval for phase II compounds negativelycorrelates with a respective pharmaceutical transaction when thetransaction deals with biologic compounds; and a probability ofreceiving the regulatory body approval for phase II compounds does notcorrelates with a respective pharmaceutical transaction when thetherapeutic area of the transaction is a group 6 therapeutic area(kidney/liver/metabolic).
 18. The method according to claim 15, furtherdetermining that: a probability of receiving the regulatory bodyapproval for phase III compounds negatively correlates with a respectivepharmaceutical transaction when a level of collaboration between partiesof the transaction is a license; a probability of receiving theregulatory body approval for phase III compounds negatively correlateswith a respective pharmaceutical transaction when a therapeutic area ofthe transaction is a group 1 therapeutic area(allergy/infection/anti-inflammatory/pain/dermatology), a group 4therapeutic area (blood disease/oncology), or a group 6 therapeutic area(kidney/liver/metabolic); and a probability of receiving the regulatorybody approval for phase III compounds does not correlate with arespective pharmaceutical transaction when the transaction deals withbiologic compounds.